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Computer Science > Computer Vision and Pattern Recognition

arXiv:2604.09231 (cs)
[Submitted on 10 Apr 2026]

Title:Hitem3D 2.0: Multi-View Guided Native 3D Texture Generation

Authors:Huiang He, Shengchu Zhao, Jianwen Huang, Jie Li, Jiaqi Wu, Hu Zhang, Pei Tang, Heliang Zheng, Yukun Li, Rongfei Jia
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Abstract:Although recent advances have improved the quality of 3D texture generation, existing methods still struggle with incomplete texture coverage, cross-view inconsistency, and misalignment between geometry and texture. To address these limitations, we propose Hitem3D 2.0, a multi-view guided native 3D texture generation framework that enhances texture quality through the integration of 2D multi-view generation priors and native 3D texture representations. Hitem3D 2.0 comprises two key components: a multi-view synthesis framework and a native 3D texture generation model. The multi-view generation is built upon a pre-trained image editing backbone and incorporates plug-and-play modules that explicitly promote geometric alignment, cross-view consistency, and illumination uniformity, thereby enabling the synthesis of high-fidelity multi-view images. Conditioned on the generated views and 3D geometry, the native 3D texture generation model projects multi-view textures onto 3D surfaces while plausibly completing textures in unseen regions. Through the integration of multi-view consistency constraints with native 3D texture modeling, Hitem3D 2.0 significantly improves texture completeness, cross-view coherence, and geometric alignment. Experimental results demonstrate that Hitem3D 2.0 outperforms existing methods in terms of texture detail, fidelity, consistency, coherence, and alignment.
Comments: 13 pages
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2604.09231 [cs.CV]
  (or arXiv:2604.09231v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2604.09231
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Huiang He [view email]
[v1] Fri, 10 Apr 2026 11:40:09 UTC (36,459 KB)
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